An adapted & improved validation protocol for digital pathology implementation
Bibliographic record
Abstract
Digital Pathology (DP) is transforming disease diagnosis by providing rapid and efficient analysis of tissue samples. However, ensuring the accuracy and reliability of diagnoses is crucial. This manuscript outlines University Health Network (UHN)'s journey towards the development of a customized validation protocol for implementing a digital workflow for primary clinical assessment. Drawing on guidelines from the Royal College of Pathologists (RCPath) UK and the College of American Pathologists (CAP), UHN has tailored its approach to accommodate the unique needs of its 14 subspecialty groups. Our protocol emphasizes pathologist-led self-validation, integration of diverse subspecialty cases, and a phased rollout with continuous monitoring. Additionally, the use of change management principles inspired by Leeds University (CCP) played a critical role in guiding the process, ensuring pathologists' comfort with digital workflows, and addressing subspecialty-specific challenges. This comprehensive validation protocol supports UHN's broader goals of leveraging DP for clinical practice while ensuring patient safety and data integrity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.156 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".